Simulation of the 2008 Iowa Flood using HiResFlood-UCI Model with Remote Sensing Data

Monday, 15 December 2014
Phu Nguyen, Andrea R Thorstensen, Kuo-lin Hsu, Amir AghaKouchak, Brett F Sanders and Soroosh Sorooshian, University of California Irvine, Civil and Environmental Engineering, Irvine, CA, United States
Precipitation is a key forcing variable in hydrological modeling of floods and being able to accurately observe precipitation is extremely important in mitigating flood impacts. The Global Precipitation Measurement (GPM) Mission, launched in Feb 2014 also presents an opportunity for high-quality real-time precipitation data and improved flood warnings. The PERSIANN-CCS developed by the scientists at the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine is one algorithm integrated in the IMERG of PMM/GPM.

In this research, the high resolution coupled hydrologic/hydraulic model named HiResFlood-UCI was applied to simulate the historical 2008 Iowa flood in the Cedar River basin. HiResFlood-UCI is a coupling of the NWS’s distributed hydrologic model HL-RDHM and the hydraulic model BreZo developed by the Computational Hydraulics Group at the University of California, Irvine. The model was forced with the real-time PERSIANN-CCS and NEXRAD Stage 2 precipitation data. Simulations were evaluated based on 2 criteria: hydrographs within the basin and the areal extent of the flooding. Streamflow hydrographs were compared at 7 USGS gages, and simulated inundation maps were evaluated using USDA AWiFS 56m resolution areal flood imagery.

The results show reasonable simulated hydrographs compared to USGS streamflow observations when simulating with PERSIANN-CCS and NEXRAD Stage 2 as forcing inputs. The simulation driven by NEXRAD Stage 2 slightly outperforms the PERSIANN-CCS simulation as the latter marginally underestimated the observed hydrographs. The simulation in both cases shows a good agreement (0.672 and 0.727 CSI for Stage 2 and PERSIANN-CCS simulations respectively) with the AWiFS image over the most impacted area in the Cedar Rapids region. Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.925) is lower than that of the Stage 2 simulation (0.965). As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.227) is better than that of the Stage 2 simulation (0.311).